How to use entity strategy for AI search visibility

If your business is barely showing up in ChatGPT, Google's AI Overviews, Gemini or Perplexity, your entity footprint is probably weak.

Not because the business is weak. Because the way the business describes itself is messy.

Most businesses describe themselves differently in every place they appear. One version on the homepage. Another on LinkedIn. Another in the schema markup. Another in press mentions. Another in the founder's bio. Another in the directory listings. To humans, this feels normal. To AI systems, it creates ambiguity. And when LLMs aren't sure what to make of a business, they move on to the clearer one.

That's the real problem. AI systems reward semantic clarity. The brands getting cited consistently are usually the brands that are easiest for machines to understand.

I'm Michelle Legge, founder of Everwilde One. Twenty years across content, SEO, brand storytelling and now AI visibility, including past work with brands like TEDx, Qantas and Koinly. I've spent a lot of that time on the boring foundational work that makes a brand legible to machines. This piece is the operational version of it.

What an entity footprint is

An entity is a real-world thing that AI systems can identify and categorise. A business. A person. A product. A service. A place. A concept.

Your entity footprint is the total understanding AI systems build about your brand across your website, your structured data, your author profiles, your citations, your LinkedIn, your reviews, your press mentions and your semantic relationships. It is what the machine thinks you are after it has read everything it can find about you.

For the foundations of what an entity is and why entities matter, read What is an entity in AI search visibility?. This piece is the implementation guide.

The seven moves, in order

Here's the framework. Seven moves. Not all equal.

1. Clarify your brand positioning
2. Define your core entity nodes
3. Operationalise governance
4. Build structured entity relationships
5. Strengthen external corroboration
6. Reinforce topical associations
7. Prepare for agentic AI discovery

The first three are foundational. They do most of the work. If you skip them and start with schema markup, you are decorating a building that hasn't been designed yet.

The middle three are amplifiers. They compound the foundational work. They are also where most agencies start, which is why most agencies are doing it wrong.

The seventh is forward-looking. It's the move you make once the foundations and amplifiers are solid. It's the move that wins the next era, not this one.

Now to each.

1. Clarify your brand positioning

This is the most important step. Almost every entity problem I've seen starts here.

Most brands have what I'd call semantic sprawl. The same company describes itself, in different rooms, as:

• A creative agency
• A digital consultancy
• A growth partner
• An AI studio
• A marketing company
• A content business

Each individual description sounds harmless. Together they teach the machine that nobody, including the founder, is sure what the business is.

AI systems need a clear category. You should be able to answer four questions in one consistent language:

• What are you
• Who do you help
• What category do you belong to
• What problem do you solve

The same four answers, in the same words, should appear on the homepage, in your meta description, in your schema, in your LinkedIn tagline, in your founder bios, in your podcast appearance descriptions and in your press boilerplates. Not similar answers. The same answers.

The clearer your positioning becomes, the easier entity resolution becomes.

2. Define your core entity nodes

Before you go near schema markup or any technical implementation, define the foundational entities that make up your brand.

Usually that means:

• The organisation
• The founder (or founders)
• The services
• The products
• The locations
• The authors
• The frameworks or methodologies you've named
• The categories you sit in

Treat each of these as a node in your brand graph. The key discipline: each node must be described consistently everywhere it appears. Same name. Same one-line description. Same category label. Same geographic signals. Same expertise framing.

This is where most brands fall over. The founder's LinkedIn bio says one thing. Their author page on the website says another. A press release says a third. The business's services page lists six services with one set of names; the case studies page lists five with different names. Each inconsistency is a small confidence-vote against you in the machine's understanding.

Consistency strengthens confidence. Inconsistency does the opposite.

3. Operationalise governance

This is where most consulting projects fail. Founders treat entity work as a one-off audit, fix the obvious issues, and assume the job is done.

It isn't.

An entity footprint is operational infrastructure. It drifts. New blog posts get written by new authors who use new terminology. New services get added with new names that don't match the originals. Old pages get edited and the schema doesn't get updated. Press releases describe the business in language that's months out of date. LinkedIn profiles change. Directory listings sit unmaintained. Each individual drift is small. Together, they undo months of foundational work.

You need a governance workflow. At minimum:

• Schema validation as part of any new content pushed to the site
• Profile consistency checks across LinkedIn, Crunchbase, Wikidata and major directories every quarter
• Metadata reviews on a recurring schedule
• Author governance: every byline, every bio, every author page accurate and consistent
• Semantic audits: are your category words still the right ones, used consistently
• Citation monitoring: are your third-party mentions accurate

This is no longer just SEO. It is machine-readable brand management. The brands that treat it as ongoing operational discipline will compound trust. The brands that treat it as a one-off project will watch their visibility slowly degrade.

4. Build structured entity relationships

Now we get to schema. After the foundations.

Structured data helps AI systems understand the relationships between your entities. Most businesses stop at three blocks: Organization, WebSite, WebPage. Strong entity footprints go deeper.

The pattern that matters is connected entities, not isolated blocks. For example:

• Person worksFor Organization
• Article author Person
• Organization hasOfferCatalog Services
• WebPage about Organization
• Organization founder Person

Your schema should behave like a connected graph. Not isolated blocks floating in space. Each entity has an @id. Each relationship is declared explicitly. Each node references back to the canonical version of itself.

Use specific schema types where possible. Instead of generic Article, use TechArticle if it fits. Instead of generic Organization, use ConsultingService or ProfessionalService. Instead of generic Service, use a specific service type with its own serviceType and areaServed. Specificity reduces ambiguity. Ambiguity is visibility friction.

5. Strengthen external corroboration

AI systems trust corroborated information more than isolated claims. What other people say about you matters more than what you say about yourself. Your own marketing copy is one voice. The web is thousands. AI systems are increasingly tuned to listen to the thousands.

This is what sameAs in schema is for. It tells AI systems that the entity on your site is the same entity as the one on your LinkedIn, your Crunchbase, your Wikidata, your podcast hosts, your conference bios, your GitHub (where relevant), your author pages on the publications you've written for.

One worked example. At Koinly, the entity strategy that did the most work wasn't on the brand site itself. It was the network of integration pages. Koinly had over a thousand exchange and wallet integrations, each with its own dedicated page: koinly.io/integrations/binance, koinly.io/integrations/coinbase, and so on. Each page declared the relationship: Koinly integrates with that exchange. From an entity perspective, that's a thousand declared relationships in the brand graph. When anyone asks an LLM "how do I do my crypto taxes for [any exchange]", Koinly becomes the entity the model can't avoid. Not because of clever positioning. Because of structured, declared relationships at scale.

Most businesses can't do a thousand integration pages. But the principle scales. Every relationship you declare, with corroboration on both ends, makes you harder for AI systems to ignore in the contexts where you should appear.

6. Reinforce topical associations

AI systems learn through repeated semantic relationships. If you want your brand to be associated with AI visibility, entity SEO, technical SEO, semantic search and ecommerce AI optimisation, those concepts have to appear repeatedly across your ecosystem.

Not once. Repeatedly. In your blog content. In your service pages. In your author bios. In your structured data. In your interviews and podcast appearances. In your PR. In your social presence. In linked mentions across the web.

This is why the brands that publish consistently on a coherent set of topics outperform the brands that scatter content across everything. Topical authority isn't a Google ranking factor anymore. It's an entity-trust factor for every system that reads about you.

Pick three to five topics that genuinely describe your business. Write about them seriously, repeatedly, over twelve months. Get cited on them. Speak about them. Make them part of your founder's identity. Over time, the machine starts associating you with those concepts confidently. That confidence is what gets you cited when someone asks an LLM about them.

7. Prepare for agentic AI discovery

The last one is forward-looking. Don't worry about it until you've done the first six. But know it's coming.

AI systems are moving beyond retrieval. They are moving toward action. The next generation of AI assistants don't just answer the question, they perform the task. They compare providers. They book the appointment. They make the reservation. They surface a product and complete the purchase. They execute the workflow.

For your business, this means a third requirement on top of the existing two. Right now, you need AI systems to understand you and trust you. Soon, you'll also need them to be able to act on you. That means actionable structured data:

• BuyAction for products
• ScheduleAction for appointments
• ReserveAction for bookings
• OrderAction for orders

If a machine cannot operationally understand how to act on your business, it may bypass you entirely. Not because you're not the right answer. Because you're not the actionable answer.

This isn't urgent today for most brands. It will be urgent within twelve months for some categories (travel, hospitality, ecommerce, professional services with bookable appointments). It will be urgent within twenty-four months for almost all of them.

What this looks like done well

The brands winning AI visibility are usually not the loudest. They are the clearest.

They explain themselves consistently. They structure their information properly. They reinforce semantic relationships through content. They build corroboration signals deliberately. They maintain governance over time.

None of this is glamorous. None of this is a tool you can buy. It's editorial discipline. It's information architecture. It's operational governance dressed up as marketing. It's also exactly why the brands that figure it out early will be very hard to displace by the time the rest of the market catches up.

Confidence is what drives visibility in LLMs and AI search. Confidence is built through clarity, consistency and corroboration. Do those three things well, repeatedly, over time, and AI systems will start describing your business the way you'd describe it yourself.

Which is the whole point.